{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial-Robustness-Toolbox for scikit-learn ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from art.classifiers import SklearnClassifier\n",
    "from art.attacks import ZooAttack\n",
    "from art.utils import load_mnist\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Training scikit-learn ExtraTreesClassifier and attacking with ART Zeroth Order Optimization attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_adversarial_examples(x_train, y_train):\n",
    "    \n",
    "    # Create and fit ExtraTreesClassifier\n",
    "    model = ExtraTreesClassifier()\n",
    "    model.fit(X=x_train, y=y_train)\n",
    "\n",
    "    # Create ART classifier for scikit-learn ExtraTreesClassifier\n",
    "    art_classifier = SklearnClassifier(model=model)\n",
    "\n",
    "    # Create ART Zeroth Order Optimization attack\n",
    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
    "\n",
    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
    "    x_train_adv = zoo.generate(x_train)\n",
    "\n",
    "    return x_train_adv, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(num_classes):\n",
    "    x_train, y_train = load_iris(return_X_y=True)\n",
    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
    "    y_train = y_train[y_train < num_classes]\n",
    "    x_train[:, 0][y_train == 0] *= 2\n",
    "    x_train[:, 1][y_train == 2] *= 2\n",
    "    x_train[:, 0][y_train == 0] -= 3\n",
    "    x_train[:, 1][y_train == 2] -= 2\n",
    "    \n",
    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
    "    \n",
    "    return x_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
    "\n",
    "    colors = ['orange', 'blue', 'green']\n",
    "\n",
    "    for i_class in range(num_classes):\n",
    "\n",
    "        # Plot difference vectors\n",
    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
    "\n",
    "        # Plot benign samples\n",
    "        for i_class_2 in range(num_classes):\n",
    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
    "                                 zorder=2, c=colors[i_class_2])\n",
    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
    "\n",
    "        # Show predicted probability as contour plot\n",
    "        h = .01\n",
    "        x_min, x_max = 0, 1\n",
    "        y_min, y_max = 0, 1\n",
    "\n",
    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "                                   vmin=0, vmax=1)\n",
    "        if i_class == num_classes - 1:\n",
    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
    "\n",
    "        # Plot adversarial samples\n",
    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
    "        axs[i_class].set_xlim((x_min, x_max))\n",
    "        axs[i_class].set_ylim((y_min, y_max))\n",
    "\n",
    "        axs[i_class].set_title('class ' + str(i_class))\n",
    "        axs[i_class].set_xlabel('feature 1')\n",
    "        axs[i_class].set_ylabel('feature 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Example: Iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### legend\n",
    "- colored background: probability of class i\n",
    "- orange circles: class 1\n",
    "- blue circles: class 2\n",
    "- green circles: class 3\n",
    "- red crosses: adversarial samples for class i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_classes = 2\n",
    "x_train, y_train = get_data(num_classes=num_classes)\n",
    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_classes = 3\n",
    "x_train, y_train = get_data(num_classes=num_classes)\n",
    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Example: MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Load and transform MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
    "\n",
    "n_samples_train = x_train.shape[0]\n",
    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
    "n_samples_test = x_test.shape[0]\n",
    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
    "\n",
    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
    "\n",
    "y_train = np.argmax(y_train, axis=1)\n",
    "y_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "n_samples_max = 200\n",
    "x_train = x_train[0:n_samples_max]\n",
    "y_train = y_train[0:n_samples_max]\n",
    "x_test = x_test[0:n_samples_max]\n",
    "y_test = y_test[0:n_samples_max]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Train ExtraTreesClassifier classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExtraTreesClassifier(n_estimators='warn', criterion='gini', max_depth=None, min_samples_split=2, \n",
    "                             min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', \n",
    "                             max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, \n",
    "                             bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, \n",
    "                             warm_start=False, class_weight=None)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X=x_train, y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_classifier = SklearnClassifier(model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x_train_adv = zoo.generate(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_adv = zoo.generate(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Evaluate ExtraTreesClassifier on benign and adversarial samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Training Score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_train, y_train)\n",
    "print(\"Benign Training Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Training Predicted Label: 5\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_train[0:1, :])[0]\n",
    "print(\"Benign Training Predicted Label: %i\" % prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Training Score: 0.3900\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_train_adv, y_train)\n",
    "print(\"Adversarial Training Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAPSElEQVR4nO3df5BV9XnH8c8TWKAgRLYEgpYIIqk22qxxB3RwlNYJoflHnYyxTMYhNi02lSa2dKplOpV2TId2lNSklhmoFMz4Ixq1Mh2rodRRM9WtQKhCUEmBWmS7iDv80CI/dp/+sYd2S3a/d3fvuecc9nm/Zpi9ez539zxe9cO593zvuebuAhDXx8oeAEC5KAEgOEoACI4SAIKjBIDgKAEguFJKwMwWmNlbZvZTM7urjBlSzGyvmb1hZtvMbHMF5llrZgfMbHuvbc1mttHMdmVfJ1ZsvuVm9m72GG4zsy+WON80M3vBzHaa2Q4z+2a2vRKPYWK+Qh5DK3qdgJmNkPS2pM9L2ifpNUkL3f0nhQ6SYGZ7JbW6+8GyZ5EkM7tG0geSHnL3S7Ntfymp091XZEU60d3vrNB8yyV94O73ljFTb2Y2VdJUd99qZuMlbZF0g6SvqgKPYWK+L6uAx7CMI4HZkn7q7rvd/YSkxyRdX8IcZw13f0lS5xmbr5e0Pru9Xj3/0ZSin/kqw93b3X1rdvuopJ2SzldFHsPEfIUoowTOl/Sfvb7fpwL/gQfIJf3QzLaY2eKyh+nHFHdvl3r+I5I0ueR5+rLEzF7Pni6U9nSlNzObLulySW2q4GN4xnxSAY9hGSVgfWyr2trlue7+OUm/Jun27HAXg7NK0kxJLZLaJd1X7jiSmZ0j6UlJd7j7kbLnOVMf8xXyGJZRAvskTev1/S9I2l/CHP1y9/3Z1wOSnlbPU5iq6cieS55+Tnmg5Hn+H3fvcPcud++WtEYlP4Zm1qSe/8Eedvenss2VeQz7mq+ox7CMEnhN0iwzm2FmoyT9uqQNJczRJzMbl704IzMbJ2m+pO3pnyrFBkmLstuLJD1T4iw/4/T/XJkbVeJjaGYm6UFJO919Za+oEo9hf/MV9RgWfnZAkrJTHX8laYSkte7+rcKH6IeZXaiev/0laaSkR8qez8welTRP0iRJHZLulvT3kh6X9ClJ70i6yd1LeXGun/nmqecw1iXtlXTb6effJcx3taSXJb0hqTvbvEw9z7tLfwwT8y1UAY9hKSUAoDpYMQgERwkAwVECQHCUABAcJQAEV2oJVHhJriTmq1eV56vybFKx85V9JFDpfxFivnpVeb4qzyYVOF/ZJQCgZHUtFjKzBZLuV8/Kv7919xWp+4+y0T5G4/73+5M6riaNHvL+G4356lPl+ao8m5T/fB/pQ53w4329eW/oJTCUi4NMsGafY9cNaX8Ahq7NN+mId/ZZAvU8HeDiIMAwUE8JnA0XBwFQw8g6fnZAFwfJTnUslqQxGlvH7gA0Qj1HAgO6OIi7r3b3VndvrfILMUBU9ZRApS8OAmBghvx0wN1PmdkSSc/r/y4OsiO3yQAUop7XBOTuz0p6NqdZAJSAFYNAcJQAEBwlAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcJQAEFxdH02Os4uNTP/rHvGJSQ3d/1t/MD2Zd43tTuYXzDyQzMf+jiXz/1o5Kplvbf1+Mj/Y9WEyn/PE0mR+0e+/mszLUlcJmNleSUcldUk65e6teQwFoDh5HAn8irsfzOH3ACgBrwkAwdVbAi7ph2a2xcwW5zEQgGLV+3RgrrvvN7PJkjaa2Zvu/lLvO2TlsFiSxmhsnbsDkLe6jgTcfX/29YCkpyXN7uM+q9291d1bmzS6nt0BaIAhl4CZjTOz8advS5ovaXtegwEoRj1PB6ZIetrMTv+eR9z9uVymGqZGXDIrmfvopmS+/9pzk/mxK9PnsZs/ns5f/mz6PHnZ/vG/xyfzv/jrBcm87bJHkvmek8eS+YqOzyfz8172ZF5VQy4Bd98t6bM5zgKgBJwiBIKjBIDgKAEgOEoACI4SAIKjBIDguJ5AjrrmfS6Zr1z3QDL/dFP6/e7D3UnvSuZ/8t2vJvORH6bP01/1xJJkPv7dU8l89MH0OoKxm9uSeVVxJAAERwkAwVECQHCUABAcJQAERwkAwVECQHCsE8jR6Lf2J/MtH01L5p9u6shznNwtbb8yme/+IP25Betm/iCZH+5On+ef8p1/Sea1HLrlqmQ+6rnXkvnZebWA2jgSAIKjBIDgKAEgOEoACI4SAIKjBIDgKAEgOHMv7uznBGv2OXZdYfurms5b0+epjyxIfy7AzOXp97M/80+PDXqm3u45+MvJ/LVr0+sAug4dTuZ+VfoK9Xu/kYw1Y+G/pe+AfrX5Jh3xTusr40gACI4SAIKjBIDgKAEgOEoACI4SAIKjBIDghtU6gef3b0vmXzivpWH7zsOIST+fzLve70zmex5Jn+ffcc3aZD77z383mU9+oL7386M8da0TMLO1ZnbAzLb32tZsZhvNbFf2dWKeAwMozkCeDqyTtOCMbXdJ2uTusyRtyr4HcBaqWQLu/pKkM49Dr5e0Pru9XtINOc8FoCBDfWFwiru3S1L2dXJ+IwEoUsMvNGpmiyUtlqQxGtvo3QEYpKEeCXSY2VRJyr4e6O+O7r7a3VvdvbVJo4e4OwCNMtQS2CBpUXZ7kaRn8hkHQNFqPh0ws0clzZM0ycz2Sbpb0gpJj5vZ1yS9I+mmRg45UFVfB1BL18H36/r5k0dG1fXzn/nKT5L5e6tGpH9Bd1dd+0c5apaAuy/sJ4p7dRBgGGHZMBAcJQAERwkAwVECQHCUABAcJQAE1/BlwyjOJXe+ncxvvSx9VvfvLtiUzK+96fZkPv77ryZzVBNHAkBwlAAQHCUABEcJAMFRAkBwlAAQHCUABMc6gWGk69DhZP7+1y9J5u9sOJbM77rnoWT+R1++MZn7jz+ezKd965VkrgI/IyMSjgSA4CgBIDhKAAiOEgCCowSA4CgBIDhKAAjOvMBzrxOs2ecYVyqvqs7fuCqZP3z3vcl8xsgxde3/Mw8tSeaz1rQn81O799a1/+GszTfpiHdaXxlHAkBwlAAQHCUABEcJAMFRAkBwlAAQHCUABMc6AQyYz21J5hNW7Evmj174fF37v/iF30zmv/in6espdO3aXdf+z2Z1rRMws7VmdsDMtvfattzM3jWzbdmfL+Y5MIDiDOTpwDpJC/rY/m13b8n+PJvvWACKUrME3P0lSZ0FzAKgBPW8MLjEzF7Pni5MzG0iAIUaagmskjRTUoukdkn39XdHM1tsZpvNbPNJHR/i7gA0ypBKwN073L3L3bslrZE0O3Hf1e7e6u6tTRo91DkBNMiQSsDMpvb69kZJ2/u7L4Bqq7lOwMwelTRP0iRJHZLuzr5vkeSS9kq6zd3Tb/YW6wSGuxFTJifz/TdflMzb7rw/mX+sxt9ZX9kzP5kfvvr9ZD6cpdYJ1PzwEXdf2MfmB+ueCkAlsGwYCI4SAIKjBIDgKAEgOEoACI4SAIKreYoQw8fIC6cn81rX7T90S/pzCc793ivJfMp3DiTzj/7wVDIfa6OS+Zrp/5DMb74ifT0C37IjmQ9XHAkAwVECQHCUABAcJQAERwkAwVECQHCUABAc6wQKVO95+nrV+/sn7DmWzHfdf2Uyv7Qlvf9a6wBq+W7n5cncf/xmXb9/uOJIAAiOEgCCowSA4CgBIDhKAAiOEgCCowSA4FgnUKBGrwOoxVovTeZvf6PG+/Xnrk/m14w5MeiZBuO4n0zmr3bOSP+C7pofjRESRwJAcJQAEBwlAARHCQDBUQJAcJQAEBwlAATHOoGzyMgZFyTzf7/1vGS+/ObHkvmXzjk46JnytKyjNZm/WON6BRPXpz/3AH2reSRgZtPM7AUz22lmO8zsm9n2ZjPbaGa7sq8TGz8ugLwN5OnAKUlL3f0SSVdKut3MfknSXZI2ufssSZuy7wGcZWqWgLu3u/vW7PZRSTslnS/pekmn15Gul3RDo4YE0DiDemHQzKZLulxSm6Qp7t4u9RSFpMl5Dweg8QZcAmZ2jqQnJd3h7kcG8XOLzWyzmW0+qeNDmRFAAw2oBMysST0F8LC7P5Vt7jCzqVk+VVKfHznr7qvdvdXdW5s0Oo+ZAeRoIGcHTNKDkna6+8pe0QZJi7LbiyQ9k/94ABptIOsE5kq6RdIbZrYt27ZM0gpJj5vZ1yS9I+mmxow4fIyc/qlkfviKqcn85j97Lpn/9rlPJfNGW9qePo//yt+k1wE0r/vXZD6xm3UAjVCzBNz9R5Ksn/i6fMcBUDSWDQPBUQJAcJQAEBwlAARHCQDBUQJAcFxPYBBGTv1kMu9cOy6Zf33Gi8l84fiOQc+UpyXvXp3Mt65qSeaTfrA9mTcf5Tx/FXEkAARHCQDBUQJAcJQAEBwlAARHCQDBUQJAcKHWCZz4Qvr97Cd+rzOZL7vo2WQ+/+c+HPRMeeroOpbMr9mwNJlf/MdvJvPmQ+nz/N3JVDr1q1ck85H/vKXGb0AjcCQABEcJAMFRAkBwlAAQHCUABEcJAMFRAkBwodYJ7L0h3XlvX/ZEQ/f/wKGZyfz+F+cnc+vq78rvPS6+Z08yn9XRlsy7kmn9WAdQTRwJAMFRAkBwlAAQHCUABEcJAMFRAkBwlAAQnLl7+g5m0yQ9JOmT6nnL+Gp3v9/Mlkv6LUnvZXdd5u7JN9xPsGafY3yaOVC0Nt+kI97Z50KTgSwWOiVpqbtvNbPxkraY2cYs+7a735vXoACKV7ME3L1dUnt2+6iZ7ZR0fqMHA1CMQb0mYGbTJV0u6fT60yVm9rqZrTWziTnPBqAAAy4BMztH0pOS7nD3I5JWSZopqUU9Rwr39fNzi81ss5ltPqnjOYwMIE8DKgEza1JPATzs7k9Jkrt3uHuXu3dLWiNpdl8/6+6r3b3V3VubNDqvuQHkpGYJmJlJelDSTndf2Wv71F53u1FS+iNpAVTSQM4OzJV0i6Q3zGxbtm2ZpIVm1iLJJe2VdFtDJgTQUAM5O/AjSX2dX0xfhB/AWYEVg0BwlAAQHCUABEcJAMFRAkBwlAAQHCUABEcJAMFRAkBwlAAQHCUABEcJAMFRAkBwlAAQHCUABFfzcwdy3ZnZe5L+o9emSZIOFjbA4DFffao8X5Vnk/Kf7wJ3/0RfQaEl8DM7N9vs7q2lDVAD89WnyvNVeTap2Pl4OgAERwkAwZVdAqtL3n8tzFefKs9X5dmkAucr9TUBAOUr+0gAQMkoASA4SgAIjhIAgqMEgOD+B66bXTssbF+7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Training Predicted Label: 3\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Test Score: 0.6500\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_test, y_test)\n",
    "print(\"Benign Test Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Test Predicted Label: 7\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_test[0:1, :])[0]\n",
    "print(\"Benign Test Predicted Label: %i\" % prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Test Score: 0.2750\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_test_adv, y_test)\n",
    "print(\"Adversarial Test Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Test Predicted Label: 9\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
   ]
  }
 ],
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